[jira] [Resolved] (SPARK-3188) Add Robust Regression Algorithm with Tukey bisquare weight function (Biweight Estimates)

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-3188?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-3188.
------------------------------
          Resolution: Won't Fix
    Target Version/s:   (was: 1.5.0)

The PR hasn't been updated and this has been pushed across 3 minor versions.

> Add Robust Regression Algorithm with Tukey bisquare weight  function (Biweight Estimates) 
> ------------------------------------------------------------------------------------------
>
>                 Key: SPARK-3188
>                 URL: https://issues.apache.org/jira/browse/SPARK-3188
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Fan Jiang
>            Assignee: Fan Jiang
>            Priority: Minor
>              Labels: features
>   Original Estimate: 0h
>  Remaining Estimate: 0h
>
> Linear least square estimates assume the error has normal distribution and can behave badly when the errors are heavy-tailed. In practical we get various types of data. We need to include Robust Regression to employ a fitting criterion that is not as vulnerable as least square.
> The Tukey bisquare weight function, also referred to as the biweight function, produces an M-estimator that is more resistant to regression outliers than the Huber M-estimator (Andersen 2008: 19).



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